class_docstr: Callback for executing different checkpoint requirements.
code: def on_update_end(self, **kwargs):
      if self.trainer.num_updates % self.checkpoint_interval == 0:
          logger.info("Checkpoint time. Saving a checkpoint.")
          # Consolidate the state dict of sharded optimizers
          consolidate_optim_state_dict(self.trainer.optimizer)
          self._checkpoint.save(
              self.trainer.num_updates,
              self.trainer.current_iteration,
              update_best=False,
          )
  
file_path: mmf/mmf/trainers/callbacks/checkpoint.py
imports: import logging
  from mmf.trainers.callbacks.base import Callback
  from mmf.utils.checkpoint import Checkpoint, consolidate_optim_state_dict
label_desc: Use image node_id 4 for calling the CheckpointCallback obj's underlying member method code with example usage: obj.on_update_end() without return types